Cancer Medicines: What Is Essential and Affordable in India?
Bibliographic record
Abstract
PURPOSE The WHO essential medicines list (EML) guides selection of drugs for national formularies. Here, we evaluate which medicines are considered highest priority by Indian oncologists and the extent to which they are available in routine practice. METHODS This is a secondary analysis of an electronic survey developed by the WHO EML Cancer Medicine Working Group. The survey was distributed globally using a hierarchical snowball method to physicians who prescribe systemic anticancer therapy. The survey captured the 10 medicines oncologists considered highest priority for population health and their availability in routine practice. RESULTS The global study cohort included 948 respondents from 82 countries; 98 were from India and 67 were from other low- and middle-income countries. Compared with other low- and middle-income countries, the Indian cohort was more likely to be medical oncologist (70% v 31%, P < .001) and work exclusively in the private health system (52% v 17%, P < .001). 14/20 most commonly selected medicines were conventional cytotoxic drugs. Universal access to these medicines was reported by a minority of oncologists; risks of significant out-of-pocket expenditures for each medicine were reported by 19%-58% of oncologists. Risk of catastrophic expenditure was reported by 58%-67% of oncologists for rituximab and trastuzumab. Risks of financial toxicity were substantially higher within the private health system compared with the public system. CONCLUSION Most high-priority cancer medicines identified by Indian oncologists are generic chemotherapy agents that provide substantial improvements in survival and are already included in WHO EML. Access to these treatments remains limited by major financial burdens experienced by patients. This is particularly acute within the private health system. Strategies are urgently needed to ensure that high-quality cancer care is affordable and accessible to all patients in India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".